Humans are fully committed to the use of vision for their social interaction. For example, the extended childhood of humans teaches us to read very subtle facial expressions that can tell us whether a person is lying, angry, embarrassed, skeptical, or in a hurry. Further, vision is at the center of another important human skill: the capacity to use our hands in countless ways. Not only do we use vision to direct our hands during social interaction, but we are also able take this much further in the use of tools, participation in sports, or the playing musical instruments. This human exceptionalism rests atop an impressive visual repertoire shared by many other mammalian species, who use often use vision to recognize territories, build nests, avoid predators, and find mates. While these everyday problems of animals are sometimes downplayed by researchers as simply involving innate behaviors, the visual problems involved are often highly complex and share much in common with human vision. While much has been learned about the visual brain, many of the basic problems of vision, and their bearing on how we see and interpret others and the world around us, remain poorly understood. Much of our experimental work on visual perception centers on how the brain analyzes social stimuli and scenes. For vision, this process necessarily begins in the retina, whose contents are interpreted though a series of highly specialized cortical areas. The brain uses this information not only to recognize objects, but also to create a three-dimensional, internal representation of the world for perception and action. It performs this operation fluidly, maintaining a stable visual scene while somehow resisting distraction by the continuous retinal disturbances caused by our own movements, most notably changes in eye gaze. In one major project, we have been investigating how neurons in the macaque temporal cortex respond during free-running paradigm that depart from the conventional mode of testing, which is the serial presentation of briefly flashed image stimuli. During this testing, we allow subjects to view natural videos playing out over several minutes at a time. Subjects are free to scan the content of the videos, as we record their gaze and the activity of many neurons in different regions of the high-level visual cortex. Our analysis also departs from convention in that we do not focus on stimulus representation per se, but rather on how different parts of the brain work together in processing different types of scenes. For this, we combined our multiple single-unit recordings with data from functional MRI (fMRI), in which macaque subjects watched the same natural videos. Given the full-brain coverage afforded by fMRI, we were then able obtain whole-brain maps of fMRI data using single-unit activity as a sort of seed or regressor. The first paper using this method was published emphasized the local response diversity within a local population of temporal cortex neurons. Perhaps most surprisingly, it demonstrated that neighboring neurons participated in very different whole-brain networks when analyzed through the seed correlation method mentioned above. A second manuscript on this topic, comparing local populations in multiple temporal cortex areas, is currently under preparation. In another project, we have investigated the nature of the temporal structure during the flow of a natural scene. Specifically, we have asked the question whether the temporal dynamics are themselves important for determining in neural selectivity. To this end, we extracted one-second segments from a continuous movie and compared the neural responses to the extracted components to those arising when the movie was shown intact. Our results indicate that temporal integration from moment to moment is an important determinant in the firing of many neurons. Further, the initial visual response transient showed a stimulus tuning that was uncorrelated with the neurons response to the same moments of the intact movie. This latter finding was very surprising and may have profound consequences for how we think about the visual brain, since most of our understanding of the cortical microcircuit, visual hierarchy, stimulus selectivity, and functional architecture are derived from experiments in which stimuli were flashed briefly onto a screen. In our studies of the pulvinar, we have continued to investigate the spatial distribution of neurons responsive to particular visual features across this large nuclear complex. In one study, we measured a concentration of neurons in and around the corticotectal tract that are notably selective for faces. In a manuscript currently under preparation, we have compared the nature of these face-selective responses with so-called face-cells in the fMRI mapped face patches of the temporal cortex. One of the principal findings is that many of the pulvinar face-selective neurons respond much earlier than the earliest responses in the face patches, suggesting that their face selectivity cannot be simply inherited from those areas. In another study, we measured the responses of pulvinar neurons to natural videos, as described above for face patches. We found a number of pulvinar neurons that exhibit similar time courses and fMRI-mapping profiles to the face patch neurons. This observation, together with their very short response latencies, raises important questions about the role of the pulvinar in the processing of certain, specialized stimuli such as faces. One possibility is that pulvinar neurons have two important roles: first, they pass such visual signals up to the cortex through a secondary visual pathway, and second they receive ample cortical input to coordinate the fast pulvinar pathway with the slower geniculocortical pathway.